Parastoo Sadeghi

Australian National University, Canberra, Australian Capital Territory, Australia

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Publications (161)114.28 Total impact

  • Ahmed Douik · Mohammad S. Karim · Parastoo Sadeghi · Sameh Sorour
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    ABSTRACT: Consider a radio access network wherein a base-station is required to deliver a set of order-constrained messages to a set of users over independent erasure channels. This paper studies the delivery time reduction problem using instantly decodable network coding (IDNC). Motivated by time-critical and order-constrained applications, the delivery time is defined, at each transmission, as the number of undelivered messages. The delivery time minimization problem being computationally intractable, most of the existing literature on IDNC propose sub-optimal online solutions. This paper suggests a novel method for solving the problem by introducing the delivery delay as a measure of distance to optimality. An expression characterizing the delivery time using the delivery delay is derived, allowing the approximation of the delivery time minimization problem by an optimization problem involving the delivery delay. The problem is, then, formulated as a maximum weight clique selection problem over the IDNC graph wherein the weight of each vertex reflects its corresponding user and message's delay. Simulation results suggest that the proposed solution achieves lower delivery and completion times as compared to the best-known heuristics for delivery time reduction.
    No preview · Article · Jan 2016
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    Parastoo Sadeghi
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    ABSTRACT: Index coding is often studied with the assumption that a single source has all the messages requested by the receivers. We refer to this as \emph{centralized} index coding. In contrast, this paper focuses on \emph{distributed} index coding and addresses the following question: How does the availability of messages at distributed sources (storage nodes) affect the solutions and achievable rates of index coding? An extension to the work of Arbabjolfaei et al. in ISIT 2013 is presented when distributed sources communicate via a semi-deterministic multiple access channel (MAC) to simultaneous receivers. A numbers of examples are discussed that show the effect of message distribution and redundancy across the network on achievable rates of index coding and motivate future research on designing practical network storage codes that offer high index coding rates.
    Preview · Article · Dec 2015
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    Shama Naz Islam · Salman Durrani · Parastoo Sadeghi

    Full-text · Article · Dec 2015 · Physical Communication
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    Shama N. Islam · Salman Durrani · Parastoo Sadeghi
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    ABSTRACT: In this paper, we consider a functional decode and forward (FDF) multi-way relay network (MWRN) where a common user facilitates each user in the network to obtain messages from all other users. We propose a novel user pairing scheme, which is based on the principle of selecting a common user with the best average channel gain. This allows the user with the best channel conditions to contribute to the overall system performance. Assuming lattice code based transmissions, we derive upper bounds on the average common rate capacity and the average sum rate with the proposed pairing scheme. Considering binary phase shift keying modulation as the simplest case of lattice code transmission, we derive asymptotic average bit error rate (BER) of the MWRN. We show that in terms of the achievable rates, the proposed pairing scheme outperforms the existing pairing schemes under a wide range of channel scenarios. The proposed pairing scheme also has lower average BER compared to existing schemes. We show that overall, the MWRN performance with the proposed pairing scheme is more robust, compared to existing pairing schemes, especially under worst case channel conditions when majority of users have poor average channel gains.
    Full-text · Article · Dec 2015 · Physical Communication
  • Noman Akbar · Nan Yang · Parastoo Sadeghi · Rodney A. Kennedy
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    ABSTRACT: We propose a novel pilot sequence design to mitigate pilot contamination in multi-cell multiuser massive multiple-input multiple-output networks. Our proposed design generates pilot sequences for all users in the multi-cell network and devises power allocation at base stations (BSs) for downlink transmission. The pilot sequences together with the power allocation ensure that the user capacity of the network is achieved and the predefined signal-to-interference-plus-noise ratio (SINR) requirements of all users are met. To realize our design, we first derive new closed-form expressions for the user capacity and the capacity region of the network. Built upon these expressions, we then develop a new algorithm to obtain the required pilot sequences and power allocation. We further determine the minimum number of antennas required at BSs to achieve certain SINR requirements of all users. Numerical results are presented to corroborate our analysis and to explicitly examine the impact of key parameters, such as the pilot sequence length and the total number of users, on the network performance. A pivotal conclusion is reached that our design achieves a larger capacity region, supports a more diverse range of SINR requirements, and needs a lower number of antennas at BSs to fulfill the predefined SINR requirements than the existing designs.
    No preview · Article · Nov 2015
  • Ni Ding · Chung Chan · Tie Liu · Rodney A. Kennedy · Parastoo Sadeghi
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    ABSTRACT: We propose a coalition game model for the problem of communication for omniscience (CO). In this game model, the core contains all achievable rate vectors for CO with sum-rate being equal to a given value. Any rate vector in the core distributes the sum-rate among users in a way that makes all users willing to cooperate in CO. We give the necessary and sufficient condition for the core to be nonempty. Based on this condition, we derive the expression of the minimum sum-rate for CO and show that this expression is consistent with the results in multivariate mutual information (MMI) and coded cooperative data exchange (CCDE). We prove that the coalition game model is convex if the sum-rate is no less than the minimal value. In this case, the core is non-empty and a rate vector in the core that allocates the sum-rate among the users in a fair manner can be found by calculating the Shapley value.
    No preview · Article · Oct 2015
  • Mohammad S. Karim · Sameh Sorour · Parastoo Sadeghi
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    ABSTRACT: In this paper, we study the problem of distributing a real-time video sequence to a group of partially connected cooperative wireless devices using instantly decodable network coding (IDNC). In such a scenario, the coding conflicts occur to service multiple devices with an immediately decodable packet and the transmission conflicts occur from simultaneous transmissions of multiple devices. To avoid these conflicts, we introduce a novel IDNC graph that represents all feasible coding and transmission conflict-free decisions in one unified framework. Moreover, a real-time video sequence has a hard deadline and unequal importance of video packets. Using these video characteristics and the new IDNC graph, we formulate the problem of minimizing the mean video distortion before the deadline as a finite horizon Markov decision process (MDP) problem. However, the backward induction algorithm that finds the optimal policy of the MDP formulation has high modelling and computational complexities. To reduce these complexities, we further design a two-stage maximal independent set selection algorithm, which can efficiently reduce the mean video distortion before the deadline. Simulation results over a real video sequence show that our proposed IDNC algorithms improve the received video quality compared to the existing IDNC algorithms.
    No preview · Article · Aug 2015
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    Ni Ding · Parastoo Sadeghi · Rodney A. Kennedy
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    ABSTRACT: This paper considers a cross-layer adaptive modulation system that is modeled as a Markov decision process (MDP). We study how to utilize the monotonicity of the optimal transmission policy to relieve the computational complexity of dynamic programming (DP). In this system, a scheduler controls the bit rate of the m-quadrature amplitude modulation (m-QAM) in order to minimize the long-term losses incurred by the queue overflow in the data link layer and the transmission power consumption in the physical layer. The work is done in two steps. Firstly, we observe the L-natural-convexity and submodularity of DP to prove that the optimal policy is always nondecreasing in queue occupancy/state and derive the sufficient condition for it to be nondecreasing in both queue and channel states. We also show that, due to the L-natural-convexity of DP, the variation of the optimal policy in queue state is restricted by a bounded marginal effect: The increment of the optimal policy between adjacent queue states is no greater than one. Secondly, we use the monotonicity results to present two low complexity algorithms: monotonic policy iteration (MPI) based on L-natural-convexity and discrete simultaneous perturbation stochastic approximation (DSPSA). We run experiments to show that the time complexity of MPI based on L-natural-convexity is much lower than that of DP and the conventional MPI that is based on submodularity and DSPSA is able to adaptively track the optimal policy when the system parameters change.
    Full-text · Article · Aug 2015
  • Mingchao Yu · Parastoo Sadeghi · Alex Sprintson
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    ABSTRACT: Random linear network coding (RLNC) is asymptotically throughput optimal in the wireless broadcast of a block of packets from a sender to a set of receivers, but suffers from heavy computational load and packet decoding delay. To mitigate this problem while maintaining good throughput, we partition the packet block into disjoint generations after broadcasting the packets uncoded once and collecting one round of feedback about receivers' packet reception state. We prove the NP-hardness of the optimal partitioning problem by using a hypergraph coloring approach, and develop an efficient heuristic algorithm for its solution. Simulations show that our algorithm outperforms existing solutions.
    No preview · Article · Jun 2015
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    Mingchao Yu · Parastoo Sadeghi · Neda Aboutorab
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    ABSTRACT: We consider broadcasting a block of packets to multiple wireless receivers under random packet erasures using instantly decodable network coding (IDNC). The sender first broadcasts each packet uncoded once, then generates coded packets according to receivers' feedback about their missing packets. We focus on strict IDNC (S-IDNC), where each coded packet includes at most one missing packet of every receiver. But we will also compare it with general IDNC (G-IDNC), where this condition is relaxed. We characterize two fundamental performance limits of S-IDNC: 1) the number of transmissions to complete the broadcast, and 2) the average delay for a receiver to decode a packet. We derive a closed-form expression for the expected minimum number of transmissions in terms of the number of packets and receivers and the erasure probability. We prove that it is NP-hard to minimize the decoding delay of S-IDNC. We also derive achievable upper bounds on the above two performance limits. We show that G-IDNC can outperform S-IDNC %in terms of the number of transmissions without packet erasures, but not necessarily with packet erasures. Next, we design optimal and heuristic S-IDNC transmission schemes and coding algorithms with full/intermittent receiver feedback. We present simulation results to corroborate the developed theory and compare with existing schemes.
    Preview · Article · Jun 2015 · Journal on Advances in Signal Processing
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    ABSTRACT: Recent advancements in graph-based analysis and solutions of instantly decodable network coding (IDNC) trigger the interest to extend them to more complicated opportunistic network coding (ONC) scenarios, with limited increase in complexity. In this paper, we design a simple IDNC-like graph model for a specific subclass of ONC, by introducing a more generalized definition of its vertices and the notion of vertex aggregation in order to represent the storage of non-instantly-decodable packets in ONC. Based on this representation, we determine the set of pairwise vertex adjacency conditions that can populate this graph with edges so as to guarantee decodability or aggregation for the vertices of each clique in this graph. We then develop the algorithmic procedures that can be applied on the designed graph model to optimize any performance metric for this ONC subclass. A case study on reducing the completion time shows that the proposed framework improves on the performance of IDNC and gets very close to the optimal performance.
    No preview · Article · May 2015
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    Mohammad S. Karim · Parastoo Sadeghi · Sameh Sorour · Neda Aboutorab
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    ABSTRACT: In this paper, we study a real-time scalable video broadcast over wireless networks in instantly decodable network coded (IDNC) systems. Such real-time scalable video has a hard deadline and imposes a decoding order on the video layers.We first derive the upper bound on the probability that the individual completion times of all receivers meet the deadline. Using this probability, we design two prioritized IDNC algorithms, namely the expanding window IDNC (EW-IDNC) algorithm and the non-overlapping window IDNC (NOW-IDNC) algorithm. These algorithms provide a high level of protection to the most important video layer before considering additional video layers in coding decisions. Moreover, in these algorithms, we select an appropriate packet combination over a given number of video layers so that these video layers are decoded by the maximum number of receivers before the deadline. We formulate this packet selection problem as a two-stage maximal clique selection problem over an IDNC graph. Simulation results over a real scalable video stream show that our proposed EW-IDNC and NOW-IDNC algorithms improve the received video quality compared to the existing IDNC algorithms.
    Full-text · Article · Apr 2015 · Journal on Advances in Signal Processing
  • Shahriar Etemadi Tajbakhsh · Parastoo Sadeghi
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    ABSTRACT: In this paper, we introduce a system and a set of algorithms for disseminating popular content to a large group of wireless clients spread over a wide area. This area is partitioned into multiple cells and there is a base station in each cell which is able to broadcast to the clients within its radio coverage. Dissemination of information in the proposed system is hybrid in nature: Each base station broadcasts a fraction of information in the form of random linear combinations of data blocks. Then the clients cooperate by exchanging packets to obtain their desired messages while they are moving arbitrarily over the area. In this paper, fundamental trade-offs between the average information delivery completion time at the clients and different parameters of the system such as bandwidth usage by the base stations, average energy consumption by the clients and the popularity of the spread information are studied. Moreover different heuristic algorithms are proposed to control and maintain a balance over these trade-offs. Also, the more complicated case of multiple sessions where each client is interested in an arbitrary subset of sessions is considered and two variants of the basic dissemination algorithm are proposed. The performance of all the proposed algorithms is evaluated via extensive numerical experiments.
    No preview · Article · Apr 2015 · Journal of Communications and Networks
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    Mingchao Yu · Alex Sprintson · Parastoo Sadeghi
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    ABSTRACT: We consider a setting in which a sender wishes to broadcast a block of K data packets to a set of wireless receivers, where each of the receivers has a subset of the data packets already available to it (e.g., from prior transmissions) and wants the rest of the packets. Our goal is to find a linear network coding scheme that yields the minimum average packet decoding delay (APDD), i.e., the average time it takes for a receiver to decode a data packet. Our contributions can be summarized as follows. First, we prove that this problem is NP-hard by presenting a reduction from the hypergraph coloring problem. Next, we show that %\alexn{an MDS-based solution or} a random linear network coding (RLNC) provides an approximate solution to this problem with approximation ratio $2$ with high probability. Next, we present a methodology for designing specialized approximation algorithms for this problem that outperform RLNC solutions while maintaining the same throughput. In a special case of practical interest with a small number of wanted packets our solution can achieve an approximation ratio (4-2/K)/3. Finally, we conduct an experimental study that demonstrates the advantages of the presented methodology.
    Preview · Article · Mar 2015
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    Ni Ding · Rodney A. Kennedy · Parastoo Sadeghi
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    ABSTRACT: This paper considers the problem of finding the minimum sum-rate strategy in cooperative data exchange (CDE) systems. In a CDE system, there are a number of geographically close cooperative clients who send packets to help the others recover a packet set. A minimum sum-rate strategy is the strategy that achieves universal recovery (the situation when all the clients recover the whole packet set) with the the minimal sum-rate (the total number of transmissions). We propose an iterative merging (IM) algorithm that recursively merges client sets based on an estimate of the minimum sum-rate and achieves local recovery until the universal recovery is achieved. We prove that the minimum sum-rate and a corresponding strategy can be found by starting the IM algorithm with an initial lower estimate of the minimum sum-rate. We run an experiment to show that the complexity of the IM algorithm is lower than the complexity of existing deterministic algorithms.
    Full-text · Article · Mar 2015
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    Ni Ding · Rodney A. Kennedy · Parastoo Sadeghi
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    ABSTRACT: We consider the fairness in cooperative data exchange (CDE) problem among a set of wireless clients. In this system, each client initially obtains a subset of the packets. They exchange packets in order to reconstruct the entire packet set. We study the problem of how to find a transmission strategy that distributes the communication load most evenly in all strategies that have the same sum-rate (the total number of transmissions) and achieve universal recovery (the situation when all clients recover the packet set). We formulate this problem by a discrete minimization problem and prove its $M$-convexity. We show that our results can also be proved by the submodularity of the feasible region shown in previous works and are closely related to the resource allocation problems under submodular constraints. To solve this problem, we propose to use a steepest descent algorithm (SDA) based on $M$-convexity. By varying the number of clients and packets, we compare SDA with a deterministic algorithm (DA) based on submodularity in terms of convergence performance and complexity. The results show that for the problem of finding the fairest and minimum sum-rate strategy for the CDE problem SDA is more efficient than DA when the number of clients is up to five.
    Full-text · Article · Feb 2015
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    Ni Ding · Rodney A. Kennedy · Parastoo Sadeghi
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    ABSTRACT: This paper considers how to accurately estimate the minimum sum-rate so as to reduce the complexity of solving cooperative data exchange (CDE) problems. The CDE system contains a number of geographically close clients who send packets to help the others recover an entire packet set. The minimum sum-rate is the minimum value of total number of transmissions that achieves universal recovery (the situation when all the clients recover the whole packet set). Based on a necessary and sufficient condition for a supermodular base polyhedron to be nonempty, we show that the minimum sum-rate for a CDE system can be determined by a maximization over all possible partitions of the client set. Due to the high complexity of solving this maximization problem, we propose a deterministic algorithm to approximate a lower bound on the minimum sum-rate. We show by experiments that this lower bound is much tighter than those lower bounds derived in the existing literature. We also show that the deterministic algorithm prevents from repetitively running the existing algorithms for solving CDE problems so that the overall complexity can be reduced accordingly.
    Full-text · Article · Feb 2015
  • Ni Ding · Parastoo Sadeghi · Rodney Kennedy

    No preview · Article · Jan 2015 · IEEE Transactions on Wireless Communications
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    Shama N Islam · Salman Durrani · Parastoo Sadeghi
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    ABSTRACT: In this paper, we formulate and study the optimum power allocation problem in an amplify and forward (AF) multi-way relay network (MWRN). We derive closed-form analytical expressions for the optimum power allocation coefficients for the data power and the pilot power of the users and the relay to maximize the sum rate. We assume that only long term statistical channel state information (CSI) (i.e., channel variance) are available to each user and the relay. Our results show that to achieve the same sum rate, optimum power allocation requires 7 − 9 dB less transmit power compared to equal power allocation depending on users' channel conditions. In addition, we show that for optimum power allocation, the reduction in the sum rate due to channel estimation errors, becomes less compared to the case of equal power allocation. Index Terms—multi-way relay network, amplify and forward, power allocation, sum rate, channel state information.
    Full-text · Conference Paper · Dec 2014
  • P. Sadeghi · M. Yu · N. Aboutorab
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    ABSTRACT: This paper consists of two main parts. In the first part, our aim is to introduce the reader to the fundamental issue of throughput-delay tradeoff in network coding for wireless communication systems. We discuss important classes of wireless network coding in the literature and identify their advantages and disadvantages in terms of throughput and delay. We also briefly present practical design considerations such as how to control the data rate, feedback overhead, and implementation complexity. In the second part, we present a case study of the throughput-delay tradeoff in generation-based network coding for wireless broadcast. We first show how partitioning a partially-received generation of data packets into sub-generations will impact the throughout and delay.We then introduce the concepts of local- and Pareto-optimal partitioning solutions, and derive as a special case, the Pareto-optimal partitioning solution obtained from instantly decodable network coding. This solution is then used to construct a new coding framework that provides more operating points on the throughput-delay tradeoff curve compared to existing schemes in the literature. Finally, we briefly discuss the design flexibilities this framework offers, such as reduced feedback overhead.
    No preview · Article · Dec 2014

Publication Stats

1k Citations
114.28 Total Impact Points

Institutions

  • 2006-2015
    • Australian National University
      • • Research School of Engineering
      • • College of Engineering & Computer Science
      Canberra, Australian Capital Territory, Australia
  • 2012
    • University of Canberra
      Canberra, Australian Capital Territory, Australia
    • ACT, Inc.
      Iowa City, Iowa, United States
  • 2010
    • Texas A&M University
      • Department of Electrical and Computer Engineering
      College Station, Texas, United States
  • 2003-2005
    • University of New South Wales
      • School of Electrical Engineering and Telecommunications
      Kensington, New South Wales, Australia